Loading...
Finance Data Scientist - Enablement
Apple
H1B ✓On-sitesenior levelPosted April 15, 2026
About the Role
The role involves guiding Finance teams through their digital maturity journey by implementing AI-enabled decision models and automated workflows. You will act as a strategic advisor to bridge the gap between business strategy and technical implementation within a governed framework.
Requirements
Candidates must have an undergraduate degree in finance, economics, or a related field and at least 5 years of experience in SQL and Python development. Proven expertise in business analysis, stakeholder management, and organizational transformation is required to effectively lead Finance teams through technical transitions.
Full Job Description
Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and curiosity to your job and there's no telling what you could accomplish.
The FDT Enablement Engineer is a strategic advisor and hands-on enabler within our Catalyst Program, focused on driving Finance workforce transformation. This role is pivotal in evolving Finance teams from manual data preparers to AI-enabled decision partners by bridging the gap between business strategy and technology implementation.
Your primary focus will be to guide Finance teams through their maturity journey—from file-based automation to intelligent AI and predictive models—regardless of their current technical expertise. You will empower them to effectively leverage capabilities ranging from data automation and business intelligence to advanced analytics, machine learning, and AI agent creation within a governed, compliant framework.
Undergraduate degree in finance, economics, accounting or related disciplines 5+ years SQL and Python development experience, including data science and applied machine learning Proven background in business analysis, technical consulting, product management, or similar role where you’ve guided technology-driven Finance or business initiatives through transformation A rare ability to act as a “translator,” making complex technical concepts (data warehousing, automation, ML, AI agents) accessible and relevant to Finance professionals with varying technical backgrounds Demonstrated experience in stakeholder management with Finance leaders, with the ability to influence and build trust at all organizational levels Experience in organizational transformation, change management, or digital capability-building programs, particularly moving teams from manual/Excel-based processes to automated, governed workflows Understanding of Finance controls, compliance requirements (e.g., SOX), and the need for audit trails in analytical environments
Hands-on familiarity with enterprise data platforms like Dataiku, Snowflake/EDW, and data visualization tools (Tableau, Power BI, or similar) Experience with no-code/low-code platforms and visual recipe builders that enable business user adoption Understanding of AI agent creation, prompt engineering, and LLM fine-tuning for domain-specific applications Demonstrated expertise managing code promotion and environment separation, applying clean code architecture principles, developing ETL pipelines, and leveraging Git and automated testing best practices Strong knowledge of data governance and PII protection practices, especially in the context of LLM usage and AI-enabled workflows Experience building learning programs, academies, or communities of practice Experience working within agile or product-based delivery environments Familiarity with Finance operations, forecasting, controls, or FP&A processes Track record of scaling technical capabilities across large analyst populations
Description
Your primary focus will be to guide Finance teams through their maturity journey—from file-based automation to intelligent AI and predictive models—regardless of their current technical expertise. You will empower them to effectively leverage capabilities ranging from data automation and business intelligence to advanced analytics, machine learning, and AI agent creation within a governed, compliant framework.
Minimum Qualifications
Undergraduate degree in finance, economics, accounting or related disciplines 5+ years SQL and Python development experience, including data science and applied machine learning Proven background in business analysis, technical consulting, product management, or similar role where you’ve guided technology-driven Finance or business initiatives through transformation A rare ability to act as a “translator,” making complex technical concepts (data warehousing, automation, ML, AI agents) accessible and relevant to Finance professionals with varying technical backgrounds Demonstrated experience in stakeholder management with Finance leaders, with the ability to influence and build trust at all organizational levels Experience in organizational transformation, change management, or digital capability-building programs, particularly moving teams from manual/Excel-based processes to automated, governed workflows Understanding of Finance controls, compliance requirements (e.g., SOX), and the need for audit trails in analytical environments
Preferred Qualifications
Hands-on familiarity with enterprise data platforms like Dataiku, Snowflake/EDW, and data visualization tools (Tableau, Power BI, or similar) Experience with no-code/low-code platforms and visual recipe builders that enable business user adoption Understanding of AI agent creation, prompt engineering, and LLM fine-tuning for domain-specific applications Demonstrated expertise managing code promotion and environment separation, applying clean code architecture principles, developing ETL pipelines, and leveraging Git and automated testing best practices Strong knowledge of data governance and PII protection practices, especially in the context of LLM usage and AI-enabled workflows Experience building learning programs, academies, or communities of practice Experience working within agile or product-based delivery environments Familiarity with Finance operations, forecasting, controls, or FP&A processes Track record of scaling technical capabilities across large analyst populations